DBMS > Google BigQuery vs. SwayDB vs. Tarantool vs. Tigris

System Properties Comparison Google BigQuery vs. SwayDB vs. Tarantool vs. Tigris

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

A horizontally scalable, ACID transactional, document database available both as a fully managed cloud service and for deployment on self-managed infrastructure

Primary database model

Relational DBMS

Key-value store

Document store
Key-value store
Relational DBMS

Document store
Key-value store
Search engine
Time Series DBMS

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Score	1.67
Rank	#143	Overall
	#25	Document stores
	#25	Key-value stores
	#65	Relational DBMS

Score	0.09
Rank	#363	Overall
	#49	Document stores
	#54	Key-value stores
	#22	Search engines
	#38	Time Series DBMS

Website

cloud.google.com/bigquery

swaydb.simer.au

www.tarantool.io

www.tigrisdata.com

Technical documentation

cloud.google.com/bigquery/docs

www.tarantool.io/en/doc

www.tigrisdata.com/docs

Developer

Google

Simer Plaha

Tigris Data, Inc.

Initial release

2010

2018

2008

2022

Current release

2.10.0, May 2022

License

Commercial or Open Source

commercial

Open Source

GNU Affero GPL V3.0

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Scala

C and C++

Server operating systems

hosted

BSD
Linux
macOS

Linux
macOS
Windows

Data scheme

yes

schema-free

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

yes

Typing

predefined data types such as float or date

yes

string, double, decimal, uuid, integer, blob, boolean, datetime

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

SQL

Support of SQL

yes

Full-featured ANSI SQL support

APIs and other access methods

RESTful HTTP/JSON API

Open binary protocol

CLI Client
gRPC
RESTful HTTP API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java
Kotlin
Scala

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

Go
Java
JavaScript (Node.js)

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Lua, C and SQL stored procedures

Triggers

yes, before/after data modification events, on replication events, client session events

Partitioning methods

Methods for storing different data on different nodes

none

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Since BigQuery is designed for querying data

Atomic execution of operations

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

yes

Durability

Support for making data persistent

yes

yes, write ahead logging

yes, using FoundationDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

yes, full featured in-memory storage engine with persistence

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

Access rights for users and roles

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